% please textwrap! It helps svn not have conflicts across a multitude of
% lines.
%
% vim:set textwidth=78:

\section{Introduction}
Classifying text documents is an important problem since it has a wide range
of applications and ultimately aids in decision making. Most review websites
receive more reviews than the number that can be feasibly checked manually. 
Classifying techniques like Linear SVM or Multinomial Bayesian can be use to 
classify documents automatically and effectively. 
Any consumers of large volumes of text can benefit from these 
techniques and subsequently make decisions based on the results.

% maybe some more examples of varying applications. one example isn't a "wide
% range of applications"

In this report, we explore techniques to construct binary classifiers for
the Movie Review Polarity Dataset Version 2.0\cite{mr}. We investigate the effects of
feature selection, feature weighting and feature transformation on the dataset and
the resulting accuracy obtained from our classification models.

The remainder of this report is organized as follows. 
Section \ref{sec:dataset} details the preprocessing techniques we investigate.
Section \ref{sec:methods} explains the learning methods used.
Section \ref{sec:results} presents the results and evaluation of our models.
Section \ref{sec:conclusion} concludes. 
